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Computable biomedical knowledge (CBK) is: "the result of an analytic and/or deliberative process about human health, or affecting human health, that is explicit, and therefore can be represented and reasned upon using logic, formal standards, and mathematical approaches." Representing biomedical knowledge in a machine-interpretable, computable form increases its ability to be discovered, accessed, understood, and deployed. Computable knowledge artifacts can greatly advance the potential for implementation, reproducibility, or extension of the knowledge by users, who may include practitioners, researchers, and learners. Enriching computable knowledge artifacts may help facilitate reuse and translation into practice. Following the examples of 10 Simple Rules papers for scientific code, software, and applications, we present 10 Simple Rules intended to make shared computable knowledge artifacts more useful and reusable. These rules are mainly for researchers and their teams who have decided that sharing their computable knowledge is important, who wish to go beyond simply describing results, algorithms, or models via traditional publication pathways, and who want to both make their research findings more accessible, and to help others use their computable knowledge. These rules are roughly organized into 3 categories: planning, engineering, and documentation. Finally, while many of the following examples are of computable knowledge in biomedical domains, these rules are generalizable to computable knowledge in any research domain. Author summary: Computable biomedical knowledge, including algorithms, models, and evidence, is already impacting—and will likely transform—health research and healthcare delivery. Representing this knowledge in formats that can be discovered and processed by computers increases its potential for use at scale. Similarly, making these algorithms, models, etc. (aka computable knowledge artifacts) understandable and reusable by others can help facilitate reuse and translation into practice. However, as with other types of scientific software or research data, creating artifacts that are usable by and useful to others requires intentionality, and often additional time and effort. We've developed over 100 computable knowledge artifacts in diverse areas of health research and healthcare and we've learned a few things along the way. We've distilled these lessons learned into 10 Simple Rules to help researchers who would like to make their computational research products available and useful to others. Our work centers on knowledge in the biomedical domain, but we believe these 10 Simple Rules are generalizable and valuable as design principles for any researcher wishing to share computational research products. [ABSTRACT FROM AUTHOR]

The four major NIH-supported career development K awards are the K01, K08, K23, and K99, which comprise 70% of the investment allocated to the 15 K-type career awards. One measure [...]

Introduction: We present current work to develop and define a class of digital objects that facilitates patient cohort identification for clinical studies, such that these objects are Findable, Accessible, Interoperable, and Reusable (FAIR) (Wilkinson et al. 2016). Developing this class of FAIR Digital Objects (FDOs) builds on the work of several years to develop the Knowledge Grid (https://kgrid.org/), which facilitates the development, description and implementation of biomedical knowledge packaged in machine-readable and machineexecutable formats (Flynn et al. 2018). Additionally, this work aligns with the goals of the Mobilizing Computable Biomedical Knowledge (MCBK) community (https://mobilizecbk.med.umich.edu/) (Mobilizing Computable Biomedical Knowledge 2018). In this abstract, we describe our work to develop a FDO carrying a computable phenotype. Defining computable phenotypes: In biomedical informatics, 'phenotyping' describes a data-driven approach to identifying a group of individuals sharing observable characteristics of interest, generally related to a disease or condition, and a 'computable phenotype' (CP) is a machine-processable expression of a phenotypic pattern of these characteristics (Hripcsak and Albers 2018). For the purposes of this work, we are interested in CPs derived from data contained in electronic health record (EHR) systems. This includes both structured data, e.g. codes for diseases, diagnoses, procedures, or laboratory tests, and unstructured data, e.g. free text including patient histories, clinical observations, discharge summaries, and reports. Thus, we define computable phenotype FDOs (CP-FDOs) as a class of FDO that packages an executable EHR-derived CP together with documentation needed to implement and use it effectively for creating cohorts of individuals with similar observable characteristics from EHR data sets. Importance of portable and FAIR CPs: There is tremendous excitement for using real-world EHR data to discover important findings about human health and well-being. However, for discovery to happen, researchers need mechanisms like CPs to identify study cohorts for analysis. Beginning in the early 2010s, a growing literature explores various methods for the secondary use of EHR data for patient phenotyping to arrive at consistent study cohorts (Shivade et al. 2014, Banda et al. 2018). The heterogeneous nature of EHR data has inspired a wide variety of phenotyping methods, from those which rely solely on documented codes linked to terms in existing vocabularies to those which combine such codes with other concepts extracted from free text using natural language processing. Our current focus is on packaging CPs inside FDOs for classifying patients as having or not having a phenotype of interest. This can be done within an individual health system, or at scale across a clinical data research network. Using CPs for cohort identification can reduce the time and expense of traditional data set building and clincal trial recruitment, and expand the potential scope of a study population(Boland et al. 2013). Creating and validating CPs requires time, resources, and both clinical and technical expertise. One estimate is that it can take 6-10 months to develop and validate a CP (Shang et al. 2019). And, as there is no standard data model within EHRs in the United States, many CPs are designed for performance at a single site, rather than for portability, which is understood as the ability to implement a phenotype at a different site with similar performance (Shang et al. 2019). While portability is increasingly recognized as an important element of phenotyping, and there have been recent efforts to develop more portable CPs, many of these processes still require significant technical expertise at the implementation site to adapt the phenotype for use on local data. There may also be significant advantages to making CPs FAIR. These include transparency in cohort selection, and better generalizability of results. FAIR CPs may also increase the potential for robust comparisons of data from related studies, leading to better evidence synthesis to improve delivery of care and ultimately human health. Defining a new class of FDOs to hold and convey CPs: We believe that packaging validated CPs inside digital objects may alleviate many of the pressures mentioned above, and contributes to making both the processes and products of clinical research more FAIR. To this end, our current work focuses on packaging a validated CP inside a machine-processable FDO. The phenotype of interest identifies pediatric and adult patients with a rare disease (Oliverio et al. 2021), and has several features which make it ideal for transformation to an executable FDO. First, the phenotype utilizes standards to define the clinical characteristics of interest, and is based on a common data model; these features increase the potential for both interoperability and reuse. Additionally, because the phenotype has been validated across three sites, its portability has already been demonstrated. Finally, the full computable phenotype has been shared as a series of SQL queries, including scripts for patient identification, deriving statistics, and validation, which have been annotated with instructions for implementation at other sites. The goals of this work are: 1. To develop CPs as executable DOs, leveraging previous work to develop executable Knowledge Objects (KO) (Flynn et al. 2018) 2. To advance our understanding of how to define computable phenotypes as a class of FDO, including what is needed to meet the requirements of binding, abstraction, and encapsulation (Wittenburg et al. 2019) Conclusion: Computable phenotypes, packaged as FDOs, may increase the potential both for the portability of a phenotype and the reusability of data resulting from its implementation. Providing CPs as executable FDOs may also reduce barriers to portability and local implementation. In this presentation, we describe our work to develop a FDO computable phenotype from an existing validated phenotype. Lessons learned from this process will increase our understanding of both the technical requirements, and how to address necessary components of abstraction, binding, and encapsulation so that these can function as FAIR Digital Objects. [ABSTRACT FROM AUTHOR]

Introduction: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T. Methods: We examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories. Results: We defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories. Conclusion: A wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders. [ABSTRACT FROM AUTHOR]

Objective: To offer practical, evidence-informed knowledge on clinical decision support systems (CDSSs) and their utility in improving care and reducing costs in otolaryngology–head and neck surgery. This primer on CDSSs introduces clinicians to both the capabilities and the limitations of this technology, reviews the literature on current state, and seeks to spur further progress in this area. Data Sources: PubMed/MEDLINE, Embase, and Web of Science. Review Methods: Scoping review of CDSS literature applicable to otolaryngology clinical practice. Investigators identified articles that incorporated knowledge-based computerized CDSSs to aid clinicians in decision making and workflow. Data extraction included level of evidence, Osheroff classification of CDSS intervention type, otolaryngology subspecialty or domain, and impact on provider performance or patient outcomes. Conclusions: Of 3191 studies retrieved, 11 articles met formal inclusion criteria. CDSS interventions included guideline or protocols support (n = 8), forms and templates (n = 5), data presentation aids (n = 2), and reactive alerts, reference information, or order sets (all n = 1); 4 studies had multiple interventions. CDSS studies demonstrated effectiveness across diverse domains, including antibiotic stewardship, cancer survivorship, guideline adherence, data capture, cost reduction, and workflow. Implementing CDSSs often involved collaboration with health information technologists. Implications for Practice: While the published literature on CDSSs in otolaryngology is finite, CDSS interventions are proliferating in clinical practice, with roles in preventing medical errors, streamlining workflows, and improving adherence to best practices for head and neck disorders. Clinicians may collaborate with information technologists and health systems scientists to develop, implement, and investigate the impact of CDSSs in otolaryngology. [ABSTRACT FROM AUTHOR]

It is not known how often lean tools and implementation determinants frameworks or checklists are used concurrently in health care quality improvement activities. The authors systematically reviewed the literature for studies that used a lean tool along with an implementation science determinants framework (January 1999 through August 2018). Seven studies (8 publications) were identified, inclusive of 2 protocols and 6 research articles across multiple continents. All included studies used the consolidated framework for implementation research as their implementation science determinants framework. Lean tools included in more than 1 publication were process mapping (4 publications), process redesign (3 publications), and 5S standardization (2 publications). Only 1 study proposed using a lean tool concurrently with an implementation science determinants framework in the design and execution of the QI project. Few published studies utilize both an implementation science determinants framework or checklist and 1 or more lean tool in their study design. [ABSTRACT FROM AUTHOR]